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Record W4383908941 · doi:10.1787/399d2c34-en

Oxidative DNA damage leading to chromosomal aberrations and mutations

2023· report· en· W4383908941 on OpenAlexaff
Eunnara Cho, Ashley Allemang, Marc Audebert, Vinita Chauhan, Stephen D. Dertinger, Giel Hendriks, Mirjam Luijten, Francesco Marchetti, Sheroy Minocherhomji, Stefan Pfuhler, Daniel J. Roberts, Kristina Trenz, Carole L. Yauk

Bibliographic record

VenueOECD series on adverse outcome pathways · 2023
Typereport
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of OttawaHealth CanadaCarleton University
Fundersnot available
KeywordsAdverse Outcome PathwayDNA damageOxidative damageDNABiologyOxidative phosphorylationUpstream (networking)GeneticsComputational biologyOxidative stressComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

This Adverse Outcome Pathway (AOP) describes the linkage between oxidative DNA damage and irreversible genomic damage (chromosomal aberrations and mutations). DNA damage is considered an important contributor to the adverse health effects of many environmental toxicants and this AOP may thus be of widespread use to the regulatory community. Although increase in oxidative DNA damage is the molecular initiating event for this AOP, there are numerous upstream key events that can also lead to DNA oxidation. Thus, this AOP may be expanded upstream, and could be incorporated into a variety of AOP networks. Furthermore, the AOP points to critical research gaps required to establish the quantitative associations and modulating factors that connect KEs across the AOP, and highlights the utility of novel test methods in understanding and evaluating the implications of oxidative DNA damage. This AOP is referred to as AOP 296 in the Collaborative Adverse Outcome Pathway Wiki (AOP-Wiki).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.152
GPT teacher head0.384
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOECD series on adverse outcome pathwaysSame topicComputational Drug Discovery MethodsFrench-language works237,207